Multi-level charging safety maintenance methods, systems and storage media

Abstract

This invention discloses a multi-level charging safety maintenance method, system, and storage medium, relating to electric vehicle charging operation and maintenance technology. The method includes: a charging pile terminal acquiring multi-dimensional parameters measured by a sensor array and sending these parameters to an edge computing unit; the edge computing unit preprocessing the multi-dimensional parameters and sending the preprocessed feature data to a cloud management platform; the edge computing unit performing preliminary diagnosis based on the multi-dimensional parameters and a lightweight diagnostic model, and performing edge-level early warning processing based on the diagnostic results; the cloud management platform evaluating the health score of key components in the charging pile based on the received feature data; determining the remaining lifespan of the key components based on the health score; and performing preventative maintenance based on the health score and remaining lifespan. This establishes a multi-level architecture of terminal-edge-cloud, enabling safety early warning and preventative maintenance of charging safety from three levels: the charging pile terminal, the edge computing unit, and the cloud management platform, thereby improving maintenance efficiency.

Description

Technical Field

[0001] This invention relates to electric vehicle charging operation and maintenance technology, and in particular to multi-level charging safety maintenance methods, systems and storage media. Background Technology

[0002] With the increasing popularity of electric vehicles, the number of charging stations has surged, making the safety and operational reliability of charging stations a focus of industry attention.

[0003] The current charging pile maintenance plan is as follows: when a user discovers a fault in the equipment, the user contacts the customer center, the customer center generates a repair order, and maintenance personnel rush to the site to troubleshoot the fault and repair or replace the faulty components.

[0004] The aforementioned maintenance process requires repairs after a fault occurs, making it impossible to predict charging station malfunctions in advance. Because charging station maintenance happens after a fault, it inevitably impacts user experience and results in low maintenance efficiency. Therefore, how to perform pre-fault maintenance on charging stations to improve efficiency has become an urgent problem to solve. Summary of the Invention

[0005] This invention provides a multi-level charging safety maintenance method, system, and storage medium to solve the problem of low maintenance efficiency of current charging piles.

[0006] According to one aspect of the present invention, a multi-level charging safety maintenance method is provided, the method comprising:

[0007] The charging pile terminal acquires multi-dimensional parameters measured by the sensor array and sends the multi-dimensional parameters to the edge computing unit;

[0008] The edge computing unit preprocesses the multi-dimensional parameters and sends the preprocessed feature data to the cloud management platform. The edge computing unit performs a preliminary diagnosis based on the multi-dimensional parameters and a lightweight diagnostic model, and performs edge-level early warning processing based on the diagnostic results.

[0009] The cloud management platform assesses the health score of key components in the charging pile based on the received feature data; determines the remaining lifespan of the key components based on the health score; and performs preventative maintenance based on the health score and remaining lifespan.

[0010] According to another aspect of the present invention, a multi-level charging safety maintenance system is provided, the system comprising:

[0011] The charging pile terminal is used to acquire multi-dimensional parameters measured by the sensor array and send the multi-dimensional parameters to the edge computing unit.

[0012] An edge computing unit is used to preprocess the preprocessed feature data according to the multi-dimensional parameters and send the preprocessed feature data to the cloud management platform. The edge computing unit performs a preliminary diagnosis based on the multi-dimensional parameters and a lightweight diagnostic model, and performs edge-level early warning processing based on the diagnostic results.

[0013] The cloud management platform is used to assess the health score of key components in the charging pile based on the received feature data; determine the remaining lifespan of the key components based on the health score; and perform preventive maintenance based on the health score and remaining lifespan.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the multi-level charging safety maintenance method according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the multi-level charging safety maintenance method according to any embodiment of the present invention.

[0019] The technical solution of this invention involves a charging pile terminal acquiring multi-dimensional parameters measured by a sensor array and sending these parameters to an edge computing unit. The edge computing unit preprocesses the multi-dimensional parameters and sends the preprocessed feature data to a cloud management platform. The edge computing unit performs a preliminary diagnosis based on the multi-dimensional parameters and a lightweight diagnostic model, and performs edge-level early warning processing based on the diagnostic results. The cloud management platform evaluates the health score of key components in the charging pile based on the received feature data, determines the remaining lifespan of the key components based on the health score, and performs preventative maintenance based on the health score and remaining lifespan. Compared to current maintenance methods that only occur after a fault, the technical solution provided by this invention establishes a multi-level architecture of end-edge-cloud, enabling safety warnings and corresponding preventative maintenance for charging safety from three levels: the charging pile terminal, the edge computing unit, and the cloud management platform. The edge computing unit can perform a preliminary diagnosis of the multi-dimensional parameters sent by the charging pile terminal using a lightweight diagnostic model, providing early warnings of potential fault risks before a fault occurs, thus reducing the risk of failure. The cloud management platform analyzes the health scores and remaining lifespan of key components in the charging pile based on the feature data sent by the edge computing unit, and initiates preventive maintenance in advance when a fault occurs, thereby improving maintenance efficiency.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the architecture of a multi-level charging safety maintenance system provided in an embodiment of the present invention;

[0023] Figure 2 This is a flowchart illustrating a multi-level charging safety maintenance method provided in an embodiment of the present invention;

[0024] Figure 3 This is a flowchart illustrating another multi-level charging safety maintenance method provided in an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the multi-level charging safety maintenance method of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] With the widespread adoption of electric vehicles, the number of charging stations has surged, making their safety and operational reliability a key focus of the industry. The inventors discovered that current charging station maintenance solutions involve users contacting customer service when a malfunction occurs. The customer service center generates a repair order, and maintenance personnel then dispatch to the site to troubleshoot and repair or replace faulty components. This process requires maintenance after a malfunction has occurred, making it impossible to predict charging station failures in advance. Because charging station maintenance happens after a malfunction, it inevitably impacts user experience and results in low maintenance efficiency. Therefore, how to perform pre-malfunction maintenance on charging stations to improve efficiency is a pressing issue that needs to be addressed.

[0029] Figure 1 This is a schematic diagram of the structure of a multi-level charging safety maintenance system provided in an embodiment of the present invention. Figure 1 As shown, the multi-level charging safety maintenance system includes charging pile terminals, edge computing units, and a cloud management platform. Multiple charging pile terminals within a charging area communicate with one edge computing unit; each charging area can be configured with one edge computing unit. Multiple edge computing units communicate with the cloud management platform.

[0030] Figure 2 This is a flowchart illustrating a multi-level charging safety maintenance method provided in an embodiment of the present invention. Figure 3This is a flowchart illustrating another multi-level charging safety maintenance method provided in this embodiment of the invention. This embodiment is applicable to situations involving preventative maintenance of charging pile terminals. The method can be executed by a multi-level charging safety maintenance device, which can be implemented in hardware and / or software. The method includes:

[0031] Step S110: The charging pile terminal acquires the multi-dimensional parameters measured by the sensor array and sends the multi-dimensional parameters to the edge computing unit.

[0032] The charging station terminal is equipped with a sensor array. The sensors are arranged in a spatial array to form a sensor array, and the data generated by the sensor array is transmitted and processed in array form. The sensor array is used to collect electrical parameters, physical state parameters, and mechanical state parameters in real time. Electrical parameters include voltage, current, or insulation resistance. Physical state parameters include the temperature of the charging gun terminals, the ambient temperature and humidity inside the cabinet, or the speed of the cooling fan. Mechanical state parameters include the status of the electronic lock or the number of times the charging gun has been inserted and removed.

[0033] Optionally, after acquiring the multi-dimensional parameters measured by the sensor array, the charging pile terminal also includes:

[0034] The charging pile terminal obtains the contact resistance of the charging gun terminal; inputs the contact resistance and rated operating current into the resistance temperature rise prediction model, and the resistance temperature rise prediction model outputs temperature rise information; and performs terminal-level early warning based on the temperature rise information and a preset temperature rise safety threshold.

[0035] After measuring the real-time contact resistance, this resistance can be used as a core input parameter to a pre-stored resistance temperature rise prediction model. The resistance temperature rise prediction model can be a physical or data model constructed based on Joule's law, material properties, and heat dissipation conditions. The input to the resistance temperature rise prediction model is the contact resistance value (R) and the preset rated operating current (I). The output of the resistance temperature rise prediction model is the predicted maximum steady-state temperature rise during the charging process. ) or risk level. By providing different training data, the resistance temperature rise prediction model can output different content, such as the maximum steady-state temperature rise ( (or risk level). By comparing the predicted temperature rise with the safety threshold, early warnings of risks such as poor contact, oxidation, or loosening can be provided before charging begins, preventing problems before they occur.

[0036] The above implementation method can predict the temperature rise of the charging pile terminal based on the contact resistance and resistance temperature rise prediction model, and then provide early warning at the terminal level before the temperature is abnormal, so as to avoid the temperature abnormality of the charging pile terminal and improve the safety of the charging pile terminal.

[0037] Optionally, the charging pile terminal obtains the contact resistance of the charging gun terminal, which can be implemented as follows:

[0038] A precise constant current diagnostic signal is applied to the charging gun terminals via a current conductor; the voltage drop across the charging gun terminals is measured via a voltage conductor and a high-precision differential amplifier, and the contact resistance is determined based on the voltage drop and the constant current diagnostic signal.

[0039] Contact resistance can be measured using a four-wire Kelvin testing method. Specifically, a precise, weak constant current diagnostic signal is applied to the charging gun terminals through one set of wires (current wires, also known as current conductors); simultaneously, using another completely independent set of wires (voltage wires, also known as voltage conductors) and a high-precision differential amplifier, the voltage drop across the terminals is directly measured, thereby accurately calculating and identifying the "contact resistance" value. This method completely eliminates interference from external factors such as cable resistance and connection resistance, achieving precise measurement of contact resistance at the micro-ohm level.

[0040] For example, an abnormal contact resistance was detected before charging ( Based on the requested 150A current, predict the temperature rise using the following formula. If the temperature rise is predicted to reach 68... More than 50 The safety threshold is such that a warning is issued before charging begins and high-current charging is prohibited, thus nipping the risk in the bud.

[0041]

[0042] Where R is the contact resistance. To predict temperature rise.

[0043] The above implementation method can achieve accurate measurement of contact resistance by combining the Kelvin four-wire method with independent current and resistance wires, thereby improving the accuracy of contact resistance measurement.

[0044] Step S120: The edge computing unit performs preprocessing based on the multi-dimensional parameters and sends the preprocessed feature data to the cloud management platform. The edge computing unit performs preliminary diagnosis based on the multi-dimensional parameters and the lightweight diagnostic model, and performs edge-level early warning processing based on the diagnosis results.

[0045] The edge computing unit includes two implementation methods: in-pile edge and regional edge. For the in-pile edge, located within the charging pile terminal, it provides microsecond / millisecond-level rapid response and control to real-time interruption signals from the local charging pile terminal. At the algorithm level, it focuses on lightweight local rule judgment and real-time closed-loop control (such as timely contactor switching) to ensure the safety and stability of individual charging processes.

[0046] At the regional edge, it is used to aggregate and process data from all charging piles within the jurisdiction. The algorithm focuses more on cross-pile collaborative analysis and macro-level optimization, such as training or updating resistance temperature rise models based on aggregated data and performing non-real-time but computationally intensive batch processing analysis. Preliminary diagnosis and edge-level early warning processing can be performed through the following methods.

[0047] The region edge aggregates and processes raw data streams from multi-physics domain sensor arrays in real time. After preprocessing, this data is typically organized into timestamped multi-channel time-series data vectors or matrices for fusion analysis.

[0048] Optionally, the edge computing unit performs a preliminary diagnosis based on the multi-dimensional parameters and the lightweight diagnostic model, and performs edge-level early warning processing based on the diagnostic results, which can be implemented as follows:

[0049] The edge computing unit predicts abnormal contact resistance based on the electrical parameters in the multi-dimensional parameters and limits charging current based on the prediction results; the edge computing unit predicts electronic locking and wire bending risks based on the mechanical state parameters in the multi-dimensional parameters and tightens or reduces charging power based on the prediction results; the edge computing unit predicts the temperature rise slope based on the thermal parameters in the multi-dimensional parameters and reduces or stops charging based on the prediction results.

[0050] Specifically, the edge computing unit monitors the real-time value of the contact resistance and its changing trend. If the resistance value rises abnormally during charging (rather than just exceeding the absolute threshold), it can predict the deterioration of the connection point and limit the current in advance.

[0051] The edge computing unit monitors the electronic locking status signal and the cable stress / bending angle. If it detects that the cable is locked but the signal jitters or there is continuous stress at an abnormal angle, it determines that there is a risk of false locking or excessive cable bending, and prohibits or initiates reduced-power charging.

[0052] In addition to monitoring the absolute value of the charging nozzle temperature, the edge computing unit also calculates its rise rate per unit time in real time. When the rise rate exceeds the dynamic threshold (which may be adaptively calculated based on the current and ambient temperature), even if the absolute temperature has not reached the upper limit, it immediately triggers the power module to dredge or stop charging, thus achieving proactive suppression of thermal runaway.

[0053] In addition to the conventional logic mentioned above, the system can also integrate an anomaly detection model based on machine learning as unconventional intelligent logic. By analyzing the joint anomaly patterns of multiple sensor parameters (such as a slight increase in resistance accompanied by a weak abnormal sound in a specific spectrum) to identify potential new types of faults, the system can achieve predictive safety protection.

[0054] The edge computing unit described above can perform preliminary diagnosis from three aspects—electrical, mechanical, and thermal parameters—based on multi-dimensional parameters, and perform corresponding early warning processing based on the diagnosis results, thereby improving the accuracy of early warning.

[0055] Optionally, the edge computing unit performs a preliminary diagnosis based on the multi-dimensional parameters and the lightweight diagnostic model, and performs edge-level early warning processing based on the diagnostic results. This can also be implemented in the following ways:

[0056] The edge computing unit determines the ripple current based on the multi-dimensional parameters; performs fast Fourier analysis on the ripple current to obtain the equivalent series resistance; performs diagnosis based on the equivalent series resistance and a preset health baseline curve to determine whether capacitor aging exists; if capacitor aging exists, a maintenance warning is generated.

[0057] The edge computing unit determines the switching transient voltage and current waveforms based on the multi-dimensional parameters; it identifies IGBT degradation indicators based on the switching transient voltage and current waveforms, obtains the degradation indicator probability, and generates a maintenance warning based on the degradation indicator probability.

[0058] Lightweight diagnostic models are used to enable predictive maintenance based on waveform analysis. Their core value lies in identifying potential faults before they occur, that is, issuing warnings before component performance shows a measurable decline trend, but before it affects basic functions or triggers protection thresholds.

[0059] Specifically, a Fast Fourier Transform (FFT) analysis can be performed on the ripple current to calculate its equivalent series resistance (ESR). ESR is a key indicator of capacitor health, and its calculated value is compared in real time with the "health baseline - failure threshold" curve built into the model. When the ESR shows a continuous upward trend and exceeds the warning line, the model will diagnose capacitor aging, generate a maintenance warning accordingly, and proactively adjust the control strategy (such as appropriate derating and optimized modulation method) to reduce capacitor stress and delay its failure when conditions permit.

[0060] Specifically, the voltage / current waveform of the switching transient can be input into a one-dimensional convolutional neural network (1D-CNN network). This network is an end-to-end diagnostic model. Instead of "identifying the waveform" first and then making a judgment, it directly extracts deep features from the raw waveform data and outputs a comprehensive probability or health score about IGBT degradation signs (such as gate degradation and increased saturation voltage drop).

[0061] Furthermore, to achieve efficient deployment, multiple diagnostic tasks for a single physical entity (such as a power module) can be completed by an integrated lightweight model (such as a multi-task network based on TensorFlow Lite). This model shares the bottom feature extraction layer and has different diagnostic output heads at the top, thus outputting multiple diagnostic results in parallel, such as capacitor ESR status and IGBT health. For complex cross-domain diagnostics, dedicated models compiled by frameworks such as LightGBM are used in collaboration.

[0062] The above-described implementation can identify the risks of capacitor aging and insulated gate bipolar transistor (IGBT) degradation through a lightweight diagnostic model, thereby providing corresponding maintenance warnings and enabling accurate monitoring of the status of capacitors and IGBTs, thus improving maintenance accuracy.

[0063] Optionally, when the edge AI detects an anomaly in a power device, it will immediately notify the local safety controller to reduce its operating speed; when the pre-diagnosis unit detects a high risk, it will trigger the payment system to suspend transactions. This collaboration achieves true systemic security, breaking down the barriers between electrical, connectivity, and data security domains.

[0064] In accelerated life tests, the DC bus capacitors of traditional charging piles, under continuous high-load operation, suffer from temperature rise due to increased ESR, which is not effectively managed, resulting in an average lifespan of approximately 30,000 hours. This invention uses an edge computing unit to monitor capacitor ESR and ripple current in real time. When early signs of degradation are detected, the system automatically adjusts the charging strategy (such as appropriately limiting peak current and optimizing switching frequency), effectively reducing the capacitor's operating stress. Under the same accelerated test conditions, the capacitor lifespan is extended by 25%-40%, reaching approximately 40,000 hours.

[0065] Step S130: The cloud management platform evaluates the health score of key components in the charging pile based on the received feature data; determines the remaining lifespan of the key components based on the health score; and performs preventive maintenance based on the health score and remaining lifespan.

[0066] The edge computing unit sends feature data and preliminary diagnostic results to the cloud management platform. After cleaning, aligning, and performing preliminary feature extraction on the multi-dimensional data from each charging pile terminal, the edge unit forms a standardized time-series data stream and uploads it to the cloud.

[0067] The data received by the cloud management platform specifically includes: (1) Time-series data of operating parameters: such as key point temperature, operating current / voltage, contact resistance value, fan speed, switching frequency, etc. (2) Event and status logs: such as start and stop times, fault alarm records, protection action times (such as over-temperature protection triggering). (3) Diagnostic intermediate results: such as capacitor ESR estimation value from edge lightweight AI model, IGBT health score, etc.

[0068] Optionally, the cloud management platform assesses the health score of key components in the charging pile based on the received feature data; and determines the remaining lifespan of the key components based on the health score, which can be implemented as follows:

[0069] The cloud management platform determines the performance degradation-related features of key components based on the received feature data; inputs the performance degradation-related features and feature data into a health label mapping model to obtain a health score; constructs a time series based on the health score, and inputs the time series into a remaining lifespan identification model to obtain the remaining lifespan.

[0070] First, calculating a "health score" (typically a scalar of 0-100 or 0-1) for each key component (such as a filter capacitor or fan) is a process combining supervised learning and feature engineering, mainly consisting of three steps:

[0071] Step 1) Feature Construction: Extract features related to performance degradation for each component from the historical data above, such as the equivalent series resistance (ESR) trend and RMS value of ripple current of capacitors; the start-up delay and vibration spectrum changes of fans; the number of insertion and removal cycles of the nozzle and the average contact resistance, etc.

[0072] Step 2) Model Training: Train the model using historical data (especially data containing records of the entire lifecycle from normal to failure). Establish a mapping model of "multi-dimensional features → health status labels" through supervised learning (such as gradient boosting trees and deep neural networks). Health status labels are typically based on expert knowledge or historical maintenance records (such as "good," "warning," and "fault").

[0073] Step 3) Real-time scoring: Input the features extracted in real time into the trained model, and the model output is the quantified health score. A downward trend in the score is a better predictor of potential problems than a single absolute value.

[0074] Secondly, remaining useful life prediction is mainly based on the time trajectory of component degradation, which can be implemented in the following ways:

[0075] For components that have clearly failed and for which sufficient data is available (such as electrolytic capacitors), their remaining lifetime distribution can be predicted by combining physical degradation models of their ESR growth (such as the Arrhenius model) with statistical survival analysis (such as the Weibull distribution).

[0076] This is the mainstream approach for handling complex, multi-factor degradation processes. The component's health score or multi-dimensional features are treated as time series data and input into a deep learning model such as Seq2Seq, Transformer, or temporal convolutional networks. The model analyzes historical degradation "trajectories," learns their change patterns, and extrapolates to predict the number of future time points required for the component's health score to drop to the failure threshold—this is the predicted remaining lifetime. The prediction is typically a range value with a confidence interval, such as remaining lifetime: 30-45 days.

[0077] Security strategies are continuously optimized based on data from the entire network, enabling human-machine collaboration for automated machine discovery and expert-assisted decision-making.

[0078] The cloud management platform described above can accurately calculate health scores using a health tag mapping model, and obtain the remaining lifespan of key components based on the time-series sequence derived from the health scores. The trained health tag mapping model can accurately analyze the health scores of key components. Based on this, the remaining lifespan identification model can accurately calculate the remaining lifespan of key components according to the time series, thereby determining whether preventative maintenance should be performed based on the remaining lifespan of key components in the charging pile terminal, improving the accuracy of preventative maintenance.

[0079] Further steps, including evaluating the health scores of key components in the charging station based on the received feature data, also include:

[0080] The cloud management platform analyzes the logs, alarms, and performance data of the charging network using unsupervised learning algorithms to identify potential new threats.

[0081] Based on the potential new threats, generate traffic filtering rules, authentication challenge frequencies, or intrusion detection feature libraries; update the relevant parameters of the resistance temperature rise prediction model and the lightweight diagnostic model;

[0082] The relevant parameters of the resistance temperature rise prediction model are sent to the charging pile terminal; the relevant parameters of the lightweight diagnostic model are sent to the edge computing unit.

[0083] By employing unsupervised learning algorithms (such as clustering and anomaly detection models) or advanced association rule mining running on a cloud management platform, in-depth analysis is performed on aggregated network logs, alerts, and performance data. When anomaly patterns that cannot be explained by existing rules and exhibit clustering or evolution are discovered, they are marked as potential new threats.

[0084] The identified anomalous patterns can be submitted to a team of security experts for analysis. Once confirmed, the experts will abstract the patterns (such as specific anomalous data sequences or attack payload characteristics) into computable threat signatures or fault fingerprints and formulate preliminary response logic.

[0085] Based on confirmed threat signatures or fault fingerprints, corresponding security strategies and diagnostic parameters are automatically generated or developed with expert assistance. For example, for new types of network attacks, new traffic filtering rules, authentication challenge frequencies, or intrusion detection signature databases are generated. For new types of hardware faults, the warning thresholds of the diagnostic model are updated (e.g., incorporating a certain vibration spectrum energy value into fan diagnosis) or new sensor data correlation analysis rules are added.

[0086] The generated policies and parameters can be distributed to the target device via an end-to-end secure channel based on PKI / TLS 1.3 / AES-256-GCM.

[0087] Applications of edge computing units include: receiving updated diagnostic algorithm models, cross-charging pile collaboration rules, or regional blacklists. For example, updating their lightweight AI models to identify new fault waveforms; or adjusting load distribution strategies to isolate charging piles suspected of being compromised.

[0088] Applications in charging pile terminals include: receiving updated local control parameters and fast detection logic. For example, updating the coefficients of the "resistance-temperature rise" model, adjusting the delay threshold of contactor action, or loading new illegal instruction check codes to achieve immediate blocking of new types of attacks.

[0089] Through the above process, the system achieves continuous adaptive evolution from threat perception, analysis and decision-making to strategy deployment, thus building dynamic defense capabilities.

[0090] In the above embodiments, the cloud management platform can update the model parameters by identifying potential new threats, thereby updating the relevant parameters of the model used by the edge computing unit and charging pile terminal, thus improving the overall anti-threat capability of the multi-level charging safety maintenance system and enhancing its security.

[0091] The multi-level charging safety maintenance method provided in this invention involves a charging pile terminal acquiring multi-dimensional parameters measured by a sensor array and sending these parameters to an edge computing unit. The edge computing unit preprocesses the multi-dimensional parameters and sends the preprocessed feature data to a cloud management platform. The edge computing unit performs a preliminary diagnosis based on the multi-dimensional parameters and a lightweight diagnostic model, and performs edge-level early warning processing based on the diagnostic results. The cloud management platform evaluates the health score of key components in the charging pile based on the received feature data, determines the remaining lifespan of the key components based on the health score, and performs preventative maintenance based on the health score and remaining lifespan. Compared to current methods of maintenance after a fault occurs, the multi-level charging safety maintenance method provided in this invention establishes a multi-level architecture of terminal-edge-cloud, enabling safety warnings and corresponding preventative maintenance at three levels: the charging pile terminal, the edge computing unit, and the cloud management platform. The edge computing unit can perform a preliminary diagnosis of the multi-dimensional parameters sent by the charging pile terminal using a lightweight diagnostic model, providing early warnings of potential fault risks before a fault occurs, thus reducing the risk of failure. The cloud management platform analyzes the health scores and remaining lifespan of key components in the charging pile based on the feature data sent by the edge computing unit. This allows for proactive preventative maintenance in case of malfunctions, improving maintenance efficiency. By scientifically allocating and coordinating safety and diagnostic functions among the charging pile terminal, edge computing unit, and cloud management platform, a complete closed loop is achieved, from millisecond-level local blocking to long-term evolutionary optimization, fundamentally changing the traditional isolated and passive protection model.

[0092] Employing an "edge analysis, cloud decision-making" model, over 95% of raw data is analyzed and discarded in real-time at the edge. Only when anomalies are detected or model training is required are less than 1% of feature data or high-density data fragments uploaded. This reduces network bandwidth usage by over 90%, and significantly lowers cloud storage and computing costs. Furthermore, through pre-diagnosis, a clear message is displayed after the user plugs in the charging gun but before payment: "Poor contact with the charging gun; please try unplugging and replugging or contact customer service." This provides clear guidance, avoiding unnecessary waiting and frozen funds. Simultaneously, due to the significantly reduced failure rate, the probability of users finding available and reliable charging stations is significantly increased.

[0093] A year-long tracking study of 1000 traditional charging piles using existing technology revealed 215 unexpected downtimes, with an average time to repair (MTTR) of 4.5 hours and annual maintenance costs approximately 18% of the equipment value. After implementing the technical solution of this invention, under the same scale and conditions, the system using predictive maintenance proactively warned and addressed 189 potential faults, reducing the number of unexpected downtimes by 88% to 26. Because maintenance can be planned in advance, the average repair time was reduced to 1.5 hours. Annual maintenance costs are expected to decrease by more than 40% (primarily due to savings on emergency travel, expensive spare parts, and associated losses from failures).

[0094] This invention provides a multi-level charging safety maintenance system, which is applicable to situations requiring preventative maintenance of charging pile terminals, such as... Figure 1 As shown, the system includes: a charging pile terminal 1, an edge computing unit 2, and a cloud management platform 3.

[0095] The charging pile terminal 1 is used to acquire multi-dimensional parameters measured by the sensor array and send the multi-dimensional parameters to the edge computing unit 2;

[0096] Edge computing unit 2 is used to preprocess the preprocessed feature data according to the multi-dimensional parameters and send the preprocessed feature data to cloud management platform 3. The edge computing unit 2 performs preliminary diagnosis according to the multi-dimensional parameters and lightweight diagnostic model, and performs edge-level early warning processing according to the diagnosis results.

[0097] The cloud management platform 3 is used to evaluate the health score of key components in the charging pile based on the received feature data; determine the remaining lifespan of the key components based on the health score; and perform preventive maintenance based on the health score and remaining lifespan.

[0098] Based on the above embodiments, optionally, the charging pile terminal 1 is further configured to acquire the contact resistance of the charging gun terminal after acquiring the multi-dimensional parameters measured by the sensor array.

[0099] The contact resistance and rated operating current are input into the resistance temperature rise prediction model, and the resistance temperature rise prediction model outputs temperature rise information.

[0100] Terminal-level early warning is issued based on the temperature rise information and the preset temperature rise safety threshold.

[0101] Based on the above embodiments, optionally, the charging pile terminal 1 is used to obtain the contact resistance of the charging gun terminal, including:

[0102] A precise constant current diagnostic signal is applied to the charging gun terminals via a current-carrying wire.

[0103] The voltage drop across the charging gun terminals is measured using voltage wires and a high-precision differential amplifier, and the contact resistance is determined based on the voltage drop and the constant current diagnostic signal.

[0104] Based on the above embodiments, optionally, the edge computing unit 2 is used to perform preliminary diagnosis based on the multi-dimensional parameters and the lightweight diagnostic model, and to perform edge-level early warning processing based on the diagnostic results, including:

[0105] The edge computing unit 2 predicts abnormal contact resistance values ​​based on the electrical parameters in the multi-dimensional parameters, and performs charging current limiting based on the prediction results.

[0106] The edge computing unit 2 predicts the risks of electronic locking and wire bending based on the mechanical state parameters in the multi-dimensional parameters, and reduces the charging power accordingly based on the prediction results.

[0107] The edge computing unit 2 predicts the heating slope based on the thermal parameters in the multi-dimensional parameters, and reduces or stops charging based on the prediction results.

[0108] Based on the above embodiments, optionally, the edge computing unit 2 is used to perform preliminary diagnosis based on the multi-dimensional parameters and the lightweight diagnostic model, and to perform edge-level early warning processing based on the diagnostic results, including:

[0109] The edge computing unit 2 determines the ripple current based on the multi-dimensional parameters; performs fast Fourier analysis based on the ripple current to obtain the equivalent series resistance; performs diagnosis based on the equivalent series resistance and a preset health baseline curve to determine whether capacitor aging exists; if capacitor aging exists, a maintenance warning is generated.

[0110] The edge computing unit 2 determines the switching transient voltage and current waveforms based on the multi-dimensional parameters; it identifies degradation signs of the insulated gate bipolar transistor based on the switching transient voltage and current waveforms, obtains the degradation sign probability, and generates a maintenance warning based on the degradation sign probability.

[0111] Based on the above embodiments, optionally, the cloud management platform 3 is used to evaluate the health score of key components in the charging pile based on the received feature data; and to determine the remaining lifespan of the key components based on the health score, including:

[0112] The cloud management platform 3 determines the performance degradation-related characteristics of key components based on the received feature data;

[0113] The performance degradation-related features and feature data are input into the health label mapping model to obtain a health score;

[0114] A time series is constructed based on the health score, and the time series is input into the remaining lifespan identification model to obtain the remaining lifespan.

[0115] Based on the above embodiments, optionally, the cloud management platform 3 is further configured to, after evaluating the health score of key components in the charging pile based on the received feature data, analyze the logs, alarms and performance data of the charging network through an unsupervised learning algorithm to identify potential new threats;

[0116] Based on the potential new threats, generate traffic filtering rules, authentication challenge frequencies, or intrusion detection feature libraries; update the relevant parameters of the resistance temperature rise prediction model and the lightweight diagnostic model;

[0117] The relevant parameters of the resistance temperature rise prediction model are sent to the charging pile terminal 1; the relevant parameters of the lightweight diagnostic model are sent to the edge computing unit 2.

[0118] The multi-level charging safety maintenance system provided in this embodiment of the invention includes a charging pile terminal 1, which acquires multi-dimensional parameters measured by a sensor array and sends these parameters to an edge computing unit 2. The edge computing unit 2 preprocesses the multi-dimensional parameters and sends the preprocessed feature data to a cloud management platform 3. The edge computing unit 2 performs preliminary diagnosis based on the multi-dimensional parameters and a lightweight diagnostic model, and performs edge-level early warning processing based on the diagnostic results. The cloud management platform 3 evaluates the health score of key components in the charging pile based on the received feature data; determines the remaining lifespan of the key components based on the health score; and performs preventative maintenance based on the health score and remaining lifespan. Compared to current maintenance methods that only occur after a fault, the multi-level charging safety maintenance system provided in this embodiment of the invention establishes a multi-level architecture of terminal-edge-cloud, enabling safety warnings and corresponding preventative maintenance at three levels: the charging pile terminal, the edge computing unit, and the cloud management platform. The edge computing unit can perform preliminary diagnosis of the multi-dimensional parameters sent by the charging pile terminal using a lightweight diagnostic model, providing early warnings of potential fault risks before a fault occurs, thus reducing the risk of failure. The cloud management platform analyzes the health scores and remaining lifespan of key components in the charging pile based on the feature data sent by the edge computing unit, and initiates preventive maintenance in advance when a fault occurs, thereby improving maintenance efficiency.

[0119] The multi-level charging safety maintenance system provided in this embodiment of the invention can execute the multi-level charging safety maintenance method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0120] Figure 4This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 10 can be used to run edge computing units and cloud management platforms, while the processing chip integrated into the charging pile terminal is used to run the charging pile terminal's technical solution. Optionally, the electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the invention described and / or claimed herein.

[0121] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0122] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as a camera, ultrasonic sensor, infrared sensor, etc.; output unit 17, such as various types of speakers, etc.; storage unit 18, such as a disk, solid-state drive, etc.; and communication unit 19, such as a network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0123] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as multi-level charging safety maintenance methods.

[0124] In some embodiments, the multi-level charging safety maintenance method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the multi-level charging safety maintenance method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the multi-level charging safety maintenance method by any other suitable means (e.g., by means of firmware).

[0125] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0126] Computer programs for implementing the multi-level charging safety maintenance method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0127] This invention also provides a computer-readable storage medium storing computer instructions for causing a processor to execute a multi-level charging safety maintenance method, the method comprising:

[0128] The charging pile terminal acquires multi-dimensional parameters measured by the sensor array and sends the multi-dimensional parameters to the edge computing unit;

[0129] The edge computing unit preprocesses the multi-dimensional parameters and sends the preprocessed feature data to the cloud management platform. The edge computing unit performs a preliminary diagnosis based on the multi-dimensional parameters and a lightweight diagnostic model, and performs edge-level early warning processing based on the diagnostic results.

[0130] The cloud management platform assesses the health score of key components in the charging pile based on the received feature data; determines the remaining lifespan of the key components based on the health score; and performs preventative maintenance based on the health score and remaining lifespan.

[0131] Based on the above embodiments, optionally, after the charging pile terminal acquires the multi-dimensional parameters measured by the sensor array, it further includes:

[0132] The charging pile terminal obtains the contact resistance of the charging gun terminal;

[0133] The contact resistance and rated operating current are input into the resistance temperature rise prediction model, and the resistance temperature rise prediction model outputs temperature rise information.

[0134] Terminal-level early warning is issued based on the temperature rise information and the preset temperature rise safety threshold.

[0135] Based on the above embodiments, optionally, the charging pile terminal obtains the contact resistance of the charging gun terminal, including:

[0136] A precise constant current diagnostic signal is applied to the charging gun terminals via a current-carrying wire.

[0137] The voltage drop across the charging gun terminals is measured using voltage wires and a high-precision differential amplifier, and the contact resistance is determined based on the voltage drop and the constant current diagnostic signal.

[0138] Based on the above embodiments, optionally, the edge computing unit performs a preliminary diagnosis based on the multi-dimensional parameters and the lightweight diagnostic model, and performs edge-level early warning processing based on the diagnostic results, including:

[0139] The edge computing unit predicts abnormal contact resistance values ​​based on the electrical parameters in the multi-dimensional parameters, and limits charging current based on the prediction results.

[0140] The edge computing unit predicts the risks of electronic locking and wire bending based on the mechanical state parameters in the multi-dimensional parameters, and reduces the charging power accordingly based on the prediction results.

[0141] The edge computing unit predicts the heating slope based on the thermal parameters in the multi-dimensional parameters, and reduces or stops charging based on the prediction results.

[0142] Based on the above embodiments, optionally, the edge computing unit performs a preliminary diagnosis based on the multi-dimensional parameters and the lightweight diagnostic model, and performs edge-level early warning processing based on the diagnostic results, including:

[0143] The edge computing unit determines the ripple current based on the multi-dimensional parameters; performs fast Fourier analysis on the ripple current to obtain the equivalent series resistance; performs diagnosis based on the equivalent series resistance and a preset health baseline curve to determine whether capacitor aging exists; if capacitor aging exists, a maintenance warning is generated.

[0144] The edge computing unit determines the transient voltage and current waveform of the switch based on the multi-dimensional parameters; it identifies degradation signs of the insulated gate bipolar transistor based on the transient voltage and current waveform of the switch, obtains the degradation sign probability, and generates a maintenance warning based on the degradation sign probability.

[0145] Based on the above embodiments, optionally, the cloud management platform evaluates the health score of key components in the charging pile based on the received feature data; and determines the remaining lifespan of the key components based on the health score, including:

[0146] The cloud management platform determines the performance degradation-related characteristics of key components based on the received feature data.

[0147] The performance degradation-related features and feature data are input into the health label mapping model to obtain a health score;

[0148] A time series is constructed based on the health score, and the time series is input into the remaining lifespan identification model to obtain the remaining lifespan.

[0149] Based on the above embodiments, optionally, after evaluating the health score of key components in the charging pile based on the received feature data, the method further includes:

[0150] The cloud management platform analyzes the logs, alarms, and performance data of the charging network using unsupervised learning algorithms to identify potential new threats.

[0151] Based on the potential new threats, generate traffic filtering rules, authentication challenge frequencies, or intrusion detection feature libraries; update the relevant parameters of the resistance temperature rise prediction model and the lightweight diagnostic model;

[0152] The relevant parameters of the resistance temperature rise prediction model are sent to the charging pile terminal; the relevant parameters of the lightweight diagnostic model are sent to the edge computing unit.

[0153] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0154] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0155] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0156] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0157] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0158] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A multi-level charging safety maintenance method, characterized in that, include: The charging pile terminal acquires multi-dimensional parameters measured by the sensor array and sends the multi-dimensional parameters to the edge computing unit; The edge computing unit preprocesses the multi-dimensional parameters and sends the preprocessed feature data to the cloud management platform. The edge computing unit performs a preliminary diagnosis based on the multi-dimensional parameters and a lightweight diagnostic model, and performs edge-level early warning processing based on the diagnostic results. The cloud management platform assesses the health score of key components in the charging pile based on the received feature data; determines the remaining lifespan of the key components based on the health score; and performs preventative maintenance based on the health score and remaining lifespan.

2. The method according to claim 1, characterized in that, After acquiring the multi-dimensional parameters measured by the sensor array, the charging pile terminal also includes: The charging pile terminal obtains the contact resistance of the charging gun terminal; The contact resistance and rated operating current are input into the resistance temperature rise prediction model, and the resistance temperature rise prediction model outputs temperature rise information. Terminal-level early warning is issued based on the temperature rise information and the preset temperature rise safety threshold.

3. The method according to claim 2, characterized in that, The charging pile terminal obtains the contact resistance of the charging gun terminal, including: A precise constant current diagnostic signal is applied to the charging gun terminals via a current-carrying wire. The voltage drop across the charging gun terminals is measured using voltage wires and a high-precision differential amplifier, and the contact resistance is determined based on the voltage drop and the constant current diagnostic signal.

4. The method according to claim 1, characterized in that, The edge computing unit performs a preliminary diagnosis based on the multi-dimensional parameters and the lightweight diagnostic model, and performs edge-level early warning processing based on the diagnostic results, including: The edge computing unit predicts contact resistance anomalies based on the electrical parameters in the multi-dimensional parameters, and limits charging current based on the prediction results. The edge computing unit predicts the risks of electronic locking and wire bending based on the mechanical state parameters in the multi-dimensional parameters, and reduces the charging power accordingly based on the prediction results. The edge computing unit predicts the heating slope based on the thermal parameters among the multi-dimensional parameters, and reduces or stops charging based on the prediction results.

5. The method according to claim 1, characterized in that, The edge computing unit performs a preliminary diagnosis based on the multi-dimensional parameters and the lightweight diagnostic model, and performs edge-level early warning processing based on the diagnostic results, including: The edge computing unit determines the ripple current based on the multi-dimensional parameters; performs fast Fourier analysis on the ripple current to obtain the equivalent series resistance; performs diagnosis based on the equivalent series resistance and a preset health baseline curve to determine whether capacitor aging exists; if capacitor aging exists, a maintenance warning is generated. The edge computing unit determines the transient voltage and current waveform of the switch based on the multi-dimensional parameters; it identifies degradation signs of the insulated gate bipolar transistor based on the transient voltage and current waveform of the switch, obtains the degradation sign probability, and generates a maintenance warning based on the degradation sign probability.

6. The method according to claim 1, characterized in that, The cloud management platform assesses the health score of key components in the charging pile based on the received feature data; and determines the remaining lifespan of the key components based on the health score, including: The cloud management platform determines the performance degradation-related characteristics of key components based on the received feature data. The performance degradation-related features and feature data are input into the health label mapping model to obtain a health score; A time series is constructed based on the health score, and the time series is input into the remaining lifespan identification model to obtain the remaining lifespan.

7. The method according to claim 1, characterized in that, After assessing the health scores of key components in the charging station based on the received feature data, the process also includes: The cloud management platform analyzes the logs, alarms, and performance data of the charging network using unsupervised learning algorithms to identify potential new threats. Based on the potential new threats, generate traffic filtering rules, authentication challenge frequencies, or intrusion detection feature libraries; update the relevant parameters of the resistance temperature rise prediction model and the lightweight diagnostic model; The relevant parameters of the resistance temperature rise prediction model are sent to the charging pile terminal; the relevant parameters of the lightweight diagnostic model are sent to the edge computing unit.

8. A multi-level charging safety maintenance system, characterized in that, include: The charging pile terminal is used to acquire multi-dimensional parameters measured by the sensor array and send the multi-dimensional parameters to the edge computing unit. An edge computing unit is used to preprocess the preprocessed feature data according to the multi-dimensional parameters and send the preprocessed feature data to the cloud management platform. The edge computing unit performs a preliminary diagnosis based on the multi-dimensional parameters and a lightweight diagnostic model, and performs edge-level early warning processing based on the diagnostic results. The cloud management platform is used to assess the health score of key components in the charging pile based on the received feature data; determine the remaining lifespan of the key components based on the health score; and perform preventive maintenance based on the health score and remaining lifespan.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the multi-level charging safety maintenance method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the multi-level charging safety maintenance method according to any one of claims 1-7.

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